Modeling Of Engineering Systems Research Articles

Sampled-Data H∞ Dynamic Output-Feedback Control for NCSs With Successive Packet Losses and Stochastic Sampling.

The problem of sampled-data H∞ dynamic output-feedback control for networked control systems with successive packet losses (SPLs) and stochastic sampling is investigated in this article. The aim of using sampled-data control techniques is to alleviate network congestion. SPLs that occur in the sensor-to-controller (S-C) and controller-to-actuator (C-A) channels are modeled using a packet loss model. Additionally, it is assumed that stochastic sampling follows a Bernoulli distribution. A model is established to capture the stochastic characteristics of both the SPL model and stochastic sampling. This model is crucial as it allows us to determine the probability distribution of the sampling interval between successive update instants, which is essential for stability analysis. An exponential mean-square stability condition for the constructed equivalent discrete-time stochastic system, which also guarantees the prescribed H∞ performance, is established by incorporating probability theory. The desired controller is designed using a step-by-step synthesis approach, which may offer lower design conservatism compared to some existing methods. Finally, our designed approach using a networked F-404 engine system model is validated and its merits relative to existing results are discussed. The proposed method is finally validated by employing a networked model of the F-404 engine system. Furthermore, the advantages of our method are presented in comparison to previous results.

Interpretable large-scale belief rule base for complex industrial systems modeling with expert knowledge and limited data

Complex system modeling technology is a hot topic. Nowadays, many complex industrial systems present three characteristics: multiple input indicators, limited data and interpretability requirements. With good interpretability, belief rule base (BRB) serves as an efficient tool for modeling complex systems. However, as the number of input indicators of industrial systems increases, BRB suffers from the combinatorial explosion problem, which makes it hard to generate large-scale BRB and optimize it while maintaining its interpretability. For this purpose, an interpretable large-scale BRB is proposed for complex systems with limited data, where expert knowledge can be utilized effectively. First, a framework for generating an initial large-scale BRB using expert knowledge and limited data is developed, including the determination of attribute weight, basic belief degree and rule weight. Afterwards, a new parameter optimization model is designed to reduce the burden of parameter optimization and maintain the interpretability of BRB, where the Adaptive Moment Estimation (Adam) algorithm is adopted to further improve the efficiency of large-scale parameter optimization. Finally, a health assessment case of an inertial navigation system (INS) verifies the proposed method.

Attack-compensated asynchronous output feedback control for stochastic switching systems with sojourn probability

This study addresses the problem of attack-compensated asynchronous output feedback control for stochastic switching systems with sojourn probability. Unlike traditional Markov/semi-Markov models that rely on transition probabilities, a novel switching rule is introduced that focuses on sojourn probability information associated with the target mode and sojourn time, which are easier to obtain than the well-known transition probabilities. Considering the vulnerability of stochastic switching systems to cyber-attacks, where the system state becomes unobservable and difficult to manage, a compensation-based output feedback controller framework is proposed. Using the Lyapunov method and stochastic analysis, sufficient conditions are provided to ensure system stability. Finally, the effectiveness and applicability of the developed approach are demonstrated using an F-404 aircraft engine system model.

Mittag-Leffler function based security control for fractional-order complex network system subject to deception attacks via Observer-based AETS and its applications

The goal of this paper is to investigate the security control for uncertain fractional-order delayed complex network systems under deception attacks using the Mittag-Leffler function and observer-based adaptive event-triggered scheme (AETS) with the fractional commensurate order in q ∈ (0, 1). The adaptive event-triggering scheme is used during the data transmission process from the sensors to the observer, where the triggering threshold can be dynamically modified to reduce resource waste. We make a novel model for the estimation error system that takes into account both the effects of the adaptive event-triggered scheme and the effects of deception attacks. A sufficient condition is obtained to guarantee the stochastic mean-square stability of the augmented error system using the Mittag-Leffler (M-L) functions and the Lyapunov functional method and by using the singular value decomposition (SVD) and linear matrix inequality (LMI) techniques, the co-design problem of desired observer and controller gains is found, and it is shown that the solution ensures the stability of a closed-loop uncertain fractional-order complex networked system. At the end of this study, two numerical examples and diesel engine system model are given to show that the above findings are correct.

Using VR to Validate and Visualize MBSE‐Designed Interfaces

AbstractSeveral unique challenges arise in the new field of integration between Model‐Based Systems Engineering (MBSE) models and game‐based digital twin visualizations. First, no known standardized interfaces between the MBSE model and game engine visualization are defined, which can lead to custom or stove‐piped solutions. Additionally, visualizing digital twin models in true to life scale is insufficient with typical desktop computers. The Bi‐Directional Interface Requirements Operational System Test (BIFROST) prototype, funded by the US Army Program Executive Office (PEO) Aviation, seeks to address these challenges.Progress of the BIFROST prototype is covered in the paper. The prototype aims to determine the feasibility and challenges of validating interfaces through visualizing changes to an MBSE model in a 3D game engine. Research was performed to visualize part of an uncrewed aircraft system ground control station in 3D using the Unity game engine. A Mission Control Architecture MBSE model, developed by PEO Aviation, is used to drive the digital twin of the ground control station through a set of virtual reality (VR) controls. Users can visualize, analyze, and test the human‐machine interaction of the 3D models in VR prior to real‐world system changes. This paper presents the recommended interfaces between the MBSE model and 3D engine, lessons learned, and future research areas.

Sensor Fault Reconstruction Using Robustly Adaptive Unknown-Input Observers.

Sensors are a key component in industrial automation systems. A fault or malfunction in sensors may degrade control system performance. An engineering system model is usually disturbed by input uncertainties, which brings a challenge for monitoring, diagnosis, and control. In this study, a novel estimation technique, called adaptive unknown-input observer, is proposed to simultaneously reconstruct sensor faults as well as system states. Specifically, the unknown input observer is used to decouple partial disturbances, the un-decoupled disturbances are attenuated by the optimization using linear matrix inequalities, and the adaptive technique is explored to track sensor faults. As a result, a robust reconstruction of the sensor fault as well as system states is then achieved. Furthermore, the proposed robustly adaptive fault reconstruction technique is extended to Lipschitz nonlinear systems subjected to sensor faults and unknown input uncertainties. Finally, the effectiveness of the algorithms is demonstrated using an aircraft system model and robotic arm and comparison studies.

Recommendations for the Model-Based Systems Engineering Modeling Process Based on the SysML Model and Domain Knowledge

Model-based systems engineering (MBSE) is a modeling approach used in industry to support the formalization, analysis, design, checking and verification of systems. In MBSE modeling, domain knowledge is the basis of the modeling. However, modeling does not happen overnight; it requires systematic training and a significant investment of resources. Unfortunately, many domain experts lack the expertise required for modeling, even though they know the domain well. The question arises about how to provide system modelers with domain knowledge at the right time to support the efficient completion of modeling. Since MBSE research that integrates AI is just beginning to take off, no public dataset is available. In this paper, aerospace SysML models are constructed based on spacecraft-related domain knowledge to form SysML model data. The validation rules are studied to validate the SysML model data, and combined with the concept of the recommended system, a recommendation method for the MBSE modeling process based on the knowledge and SysML model is proposed. A GLOVE language model is pre-trained by using domain knowledge and general knowledge; the model data are also used to fine-tune the GLOVE language model combined with the pre-training to recommend some domain development processes. The recommendation list is manually quality-verified and fed into the pre-training phase, while new requirement texts are continuously added in the fine-tuning phase, resulting in a more relevant and accurate recommendation list. Experiments show that the incremental recommender system can not only effectively recommend SysML models, but also improve the quality and efficiency of MBSE development.

Towards a formal ontology of engineering functions, behaviours, and capabilities

In both applied ontology and engineering, functionality is a well-researched topic, since it is through teleological causal reasoning that domain experts build mental models of engineering systems, giving birth to functions. These mental models are important throughout the whole lifecycle of any product, being used from the design phase up to diagnosis activities. Though a vast amount of work to model functions has already been carried out, the literature has not settled on a shared and well-defined approach due to the variety of concepts involved and the modeling tasks that functional descriptions should satisfy. The work in this paper posits the basis and makes some crucial steps towards a rich ontological description of functions and related concepts, such as behaviour, capability, and capacity. A conceptual analysis of such notions is carried out using the top-level ontology DOLCE as a framework, and the ensuing logical theory is formally described in first-order logic and OWL, showing how ontological concepts can model major aspects of engineering products in applications. In particular, it is shown how functions can be distinguished from the implementation methods to realize them, how one can differentiate between capabilities and capacities of a product, and how these are related to engineering functions.

Methodology for Ensuring the Viability for a Complex Object Based on Managing its Structural Dynamics

The article examines a conceptual model that forms the basis of the authors’ proposed approach to solving the problem of creating integrated information-management systems (IMS) for making managerial decisions, embedded within the structure of vertically integrated Business Entity (BE) to maintain their viability. Viability is understood as the ability of an BE to maintain key technological and business indicators within safe ranges throughout its life cycle (LC), while being subject to the influence of destructive internal and external factors. The problem of synthesizing a generalized structure of an IMS is proposed to solve using a system-cybernetic approach, which considers this problem as the managing its structural dynamics. Managing structural dynamics enables maintaining a regulated level of viability of the vertically integrated BE at all the key stages of its life cycle. The proposed structure of model-algorithmic support (MAS) implements the methods for complexity management. This methodology proposes the approach that justifies the evolutionary redistribution of roles between managerial teams of the vertically integrated BE and functionally equivalent information-management systems. The results of implementing the structure of an IMS, as an example are provided, which arises through the transformation of its original multi-structural state into a qualitatively different state. Multi-structural states are characterized by the use of various control methods, such as "Automatic Multi-Factor", "Automatic Optimal" "Manual on site" etc. In the synthesis of the IMS structure, the methods for system modeling technologies based on simulation-statistical and fuzzy-possibilistic approaches were used.

Study of high power steam turbine forced oscilla-tions near operating frequency

Problem. Forced oscillations of the turbine unit-foundation-base system near the operating frequency are considered. Goal. The purpose of the work is to study forced vibrations of the turbine unit-foundation-base system near the operating frequency range to assess the vibration state of the system and determine the causes of increased vibrations. The object of the study is the turbine unit-foundation-base system. The system consists of a single reinforced concrete foundation on which a steam turbine and generator are installed. The subject of the study is the amplitude-frequency dependences of forced oscillations of the turbine unit-foundation-base system. Methodology. The study was carried out using vibration methods and the finite element method. Also worth noting is the use of original methods developed directly by the author for constructing models of complex mechanical engineering systems. Results. As a result of the research, three-dimensional finite element models of low-pressure cylinder housings, the foundation and the entire turbine unit-foundation-base system were built. The conducted research allows us to draw a conclusion regarding the reasons for the increased level of vibration near the operating frequency and directions for their prevention. Originality. Regarding the methods for constructing a model of the turbine unit-foundation-base system, it should be noted that they are unique. The features of the applied modeling method make it possible to take into account the variable interaction between the low-pressure cylinder bodies and the foundation. Fixpoints are also taken into account. Previous studies of the turbine unit-foundation-base system did not allow us to determine the causes of increased vibrations of the shaft line supports. Practical significance. The results of the work relate to direct practical application. As a result of the work, conclusions were drawn about the ways to increase the vibration reliability of the turbine unit-foundation-base system and further directions for research.

A Digital Twin of the terrestrial water cycle: a glimpse into the future through high-resolution Earth observations

Climate change is profoundly affecting the global water cycle, increasing the likelihood and severity of extreme water-related events. Better decision-support systems are vital to accurately predict and monitor water-related environmental disasters and optimally manage water resources. These must integrate advances in remote sensing, in situ, and citizen observations with high-resolution Earth system modeling, artificial intelligence (AI), information and communication technologies, and high-performance computing. Digital Twin Earth (DTE) models are a ground-breaking solution offering digital replicas to monitor and simulate Earth processes with unprecedented spatiotemporal resolution. Advances in Earth observation (EO) satellite technology are pivotal, and here we provide a roadmap for the exploitation of these methods in a DTE for hydrology. The 4-dimensional DTE Hydrology datacube now fuses high-resolution EO data and advanced modeling of soil moisture, precipitation, evaporation, and river discharge, and here we report the latest validation data in the Mediterranean Basin. This system can now be explored to forecast flooding and landslides and to manage irrigation for precision agriculture. Large-scale implementation of such methods will require further advances to assess high-resolution products across different regions and climates; create and integrate compatible multidimensional datacubes, EO data retrieval algorithms, and models that are suitable across multiple scales; manage uncertainty both in EO data and models; enhance computational capacity via an interoperable, cloud-based processing environment embodying open data principles; and harness AI/machine learning. We outline how various planned satellite missions will further facilitate a DTE for hydrology toward global benefit if the scientific and technological challenges we identify are addressed.

A Bayesian approach for estimating the post-earthquake recovery trajectories of electric power systems in Japan

ABSTRACT Post-disaster recovery modelling of engineering systems has become an important facet of catastrophe risk modelling and management for natural hazards. The post-disaster recovery trajectory of a civil infrastructure system can be quantified using (a) the initial post-disaster functionality level, Q o ; (b) rapidity, h (i.e., the rate of functionality restoration); and (c) recovery time, R t . This study uses a Bayesian estimation approach to derive a set of probabilistic models to estimate Q o , R t , and h of electric power networks (EPNs) using post-earthquake recovery data from 16 large earthquakes in Japan between 2003 and 2022. The considered predictor (explanatory) variables include earthquake magnitude, year of occurrence, seismic intensity, and exposed population (PEX). Apart from being a simple and efficient stand-alone tool, the proposed data-driven models can be a useful benchmarking tool for simulation-based approaches for EPN recovery modelling.

Preface

With great and successful prestigious history of ICETAC, 2023 4th International Conference on Electrical Technology and Automatic Control (ICETAC 2023) was held in Chongqing, China during 24th–26th, November 2023 via virtual form. It was sponsored by School of Electrical Engineering, Chongqing University and organized by National Key Laboratory of Power Transmission and Transformation Equipment Technology.ICETAC 2023 is a great academic platform and is of great significance for disseminating cutting-edge information in disciplines and industry, promoting the transformation of scientific and technological achievements, and condensing academic achievements in the industry at this event.The Conference consisted of three plenary keynote speeches (addressed by Associate Professor Vandana Sharma from CHRIST University, India, Professor Xiaofang Yuan from Hunan University, China, and Associate Professor Qiushi Cui from Chongqing University, China, respectively), several oral and poster presentations, in which a wide range of topics were covered and the most recent significant results were presented. They discussed such topics as Unlocking the Potentials of AI in the domain of Automatic Control, Collaborative Operation Optimization of Processing Equipment and AGVs for Green Discrete Manufacturing, New Power System Modeling, Simulation and Testing Technology, etc. Besides, the attending scholars and experts exchanged deeply on the latest research achievements and advanced experience in the industry development and shared their unique insights into development trends and research directions of the industry.Some selected works presented at the Conference are published in this conference proceedings edition of Journal of Physics: Conference Series., which shows readers the latest research results and application cases of electrical and automatic control technology. We firmly believe that by reading these papers, readers will deepen their understanding of these fields and appreciate their enormous significance and potential impact on contemporary society.We wish to acknowledge all the participants, and the support of our sponsors. We would also like to take this opportunity to thank the members of Technical Program Committee and Organizing Committee, as well as the expert reviewers. What’s more, we hope that the conference series ICETAC shall continue to the next many sessions and deliver its purpose as the arena for presenting and exchanging the ideas of research in the fields of electrical and automatic control technology and anything related.The Committee of ICETAC 2023List of Committee Member is available in this pdf.

Random Vibration Analysis of an Axially Moving Viscoelastic Beams based on Complex mode and Pseudo Excitation

Axially Moving Beams can serve as simplified models for many engineering systems, such as aerial cable trams, power conveyor belts, magnetic tapes, textile fibers, aerospace vehicles, etc. They often work in open environments in the wild, with random excitations. In engineering practice, viscoelastic materials are widely used in various engineering fields. This article studies the lateral random response of axially moving viscoelastic beams under various random excitations. Under the random external excitation of concentrated and distributed loading, analytical expressions of the mean function, correlation function, power spectral density, and mean square function of the random response of an axially moving viscoelastic beam are derived. They prove the correctness and simplicity of the Pseudo-excitation method for solving the power spectral density of the random displacement response of axially moving viscoelastic beams.

Clinically informed machine learning elucidates the shape of hospice racial disparities within hospitals

Racial disparities in hospice care are well documented for patients with cancer, but the existence, direction, and extent of disparity findings are contradictory across the literature. Current methods to identify racial disparities aggregate data to produce single-value quality measures that exclude important patient quality elements and, consequently, lack information to identify actionable equity improvement insights. Our goal was to develop an explainable machine learning approach that elucidates healthcare disparities and provides more actionable quality improvement information. We infused clinical information with engineering systems modeling and data science to develop a time-by-utilization profile per patient group at each hospital using US Medicare hospice utilization data for a cohort of patients with advanced (poor-prognosis) cancer that died April-December 2016. We calculated the difference between group profiles for people of color and white people to identify racial disparity signatures. Using machine learning, we clustered racial disparity signatures across hospitals and compared these clusters to classic quality measures and hospital characteristics. With 45,125 patients across 362 hospitals, we identified 7 clusters; 4 clusters (n = 190 hospitals) showed more hospice utilization by people of color than white people, 2 clusters (n = 106) showed more hospice utilization by white people than people of color, and 1 cluster (n = 66) showed no difference. Within-hospital racial disparity behaviors cannot be predicted from quality measures, showing how the true shape of disparities can be distorted through the lens of quality measures. This approach elucidates the shape of hospice racial disparities algorithmically from the same data used to calculate quality measures.

Existence of solutions for semilinear retarded equations with non-instantaneous impulses, non-local conditions, and infinite delay

Abstract In this work, we investigate the existence and uniqueness of solutions for retarded equations with non-instantaneous impulses, non-local conditions, and infinite delay. To achieve this goal, we select an appropriate phase space that satisfies the axiomatic theory developed by Hale and Kato for studying differential equations with infinite delay. Then, we reformulate the problem of existence of solutions as the problem of finding fixed points of an operator. To this end, we apply the Karakostas fixed point theorem, which is an extension of the well-known Krasnoselskii fixed point theorem. Under certain conditions, we establish the uniqueness of solutions for our problem. Finally, we analyze the prolongation of solutions and demonstrate that, given certain conditions, these solutions are globally defined. Our research has important implications for the study of delayed dynamical systems, including models of population dynamics, physiological processes, and engineering systems.

Finite Time Stability Results for Neural Networks Described by Variable-Order Fractional Difference Equations

Variable-order fractional discrete calculus is a new and unexplored part of calculus that provides extraordinary capabilities for simulating multidisciplinary processes. Recognizing this incredible potential, the scientific community has been researching variable-order fractional discrete calculus applications to the modeling of engineering and physical systems. This research makes a contribution to the topic by describing and establishing the first generalized discrete fractional variable order Gronwall inequality that we employ to examine the finite time stability of nonlinear Nabla fractional variable-order discrete neural networks. This is followed by a specific version of a generalized variable-order fractional discrete Gronwall inequality described using discrete Mittag–Leffler functions. A specific version of a generalized variable-order fractional discrete Gronwall inequality represented using discrete Mittag–Leffler functions is shown. As an application, utilizing the contracting mapping principle and inequality approaches, sufficient conditions are developed to assure the existence, uniqueness, and finite-time stability of the equilibrium point of the suggested neural networks. Numerical examples, as well as simulations, are provided to show how the key findings can be applied.

Applying System-Theoretic Process Analysis (STPA)-based methodology supported by Systems Engineering models to a UK rail project

Systems safety in railways focuses on providing the necessary assurance that the railway system is operationally safe and meets all relevant regulatory requirements. Safety risks associated with changes in the UK railway are controlled through the Common Safety Method for Risk Evaluation and Assessment (CSM-RA). As part of the CSM-RA framework, various safety analysis methods such as Failure Modes and Effects Criticality Analysis (FMECA), Fault Tree Analysis (FTA), Event Tree Analysis (ETA) and other traditional analysis methods conducted via expert brainstorming such as Hazard Identification (HAZID) workshops have been relied upon for many years in the UK rail industry; aiming to evaluate and mitigate all reasonably foreseeable hazards. This paper reports a comparison case study of the application of a novel System-Theoretic Process Analysis (STPA)-based methodology against the traditional approach for hazard analysis in UK rail projects. The proposed methodology uses Systems Engineering (SE) models in each of its steps. The application of the novel methodology demonstrates that it is suitable for hazard identification and analysis in complex rail systems. It shows that the approach goes beyond the capabilities of traditional methods, provides insights into the interaction among system components and captures hazards within the context of the whole. The SE models used in this study prove to be valuable not only for illustrating the System of Interest (SOI) visually, but also providing a high-level understanding of the system and a more detailed understanding of component interactions. They also improved the focus, in scope, effectiveness, and efficiency of the analysis.

A system theory (STAMP) based quantitative accident analysis model for complex engineering systems

This paper is aimed to propose a hybrid accident analysis framework for complex engineering systems based on methods of the systems theoretic accident model and process (STAMP), failure modes and effects analysis (FMEA), and analytic hierarchy process (AHP). Within the scope of the proposed model, initially, an accident is qualitatively analyzed by identifying safety constraints, constructing a hierarchical control process, and specifying links between unsafe activities. As a second step, failure modes, their causes, and their consequences are determined. Lastly, the risk scores of each failure mode are calculated by analysis of obtained expert judgments. In this step, as different from the conventional FMEA, results are calculated based on the weight scores that are found according to the AHP approach of each accident factor. For the demonstration stage, a real case study is performed to present the effectiveness of the strategy. It is observed that the proposed approach allows analyzing the accident more specifically by defining each relationship between factors and root causes corresponding to the accident and prioritizing them based on the weights, which are assigned according to the accident's nature. In addition, it may be adapted for different complex engineering systems as well as it is a suitable and useful model for the accident analysis associated with smart ship concepts.

A KNOWLEDGE GRAPH AND RULE BASED REASONING METHOD FOR EXTRACTING SAPPHIRE INFORMATION FROM TEXT

AbstractRepresentation of design information using causal ontologies is very effective for creative ideation in product design. Hence researchers created databases with models of engineering and biological systems using causal ontologies. Manually building many models using technical documents requires significant effort by specialists. Researchers worked on the automatic extraction of design information leveraging the computational techniques of Machine Learning. But these methods are data intensive, have manual touch points and have not yet reported the end-to-end performance of the process. In this paper, we present the results of a new method inspired by the cognitive process followed by specialists. This method uses the Knowledge Graph with Rule based reasoning for information extraction for the SAPPhIRE causality model from natural language texts. Unlike the supervised learning methods, this new method does not require data intensive modelling. We report the performance of the end-to-end information extraction process, which is found to be a promising alternative.